Attention & Transformers Explained + Coded From Scratch in PyTorch (Multi-Head Attention) #ai #llm

Attention, multi-head attention and the full Transformer, explained from zero and coded from scratch in PyTorch, with every step worked by hand on a tiny example and every tensor shape on screen. We start with why recurrent networks struggled, turn words into vectors, and build attention one step at a time: dot products, softmax, queries, keys and values, why we divide by the square root of d_k, causal and padding masks. Then multi-head attention: splitting 512 dimensions into 8 heads of 64, the shapes after every line of code, and a check against PyTorch's own nn.MultiheadAttention. Next the rest of the Transformer: sinusoidal positional encodings, residual connections and LayerNorm, the feed-forward block, encoder and decoder layers, cross-attention and the full architecture. Finally we train it live on a GPU, decode step by step and look at the attention maps it learned. Key takeaway: attention lets every token look at every other token in one step, and that is the whole Transformer. Timeline 0:00 - 3:55 Why attention 3:55 - 7:35 Words become vectors 7:35 - 16:07 Attention, step by step 16:07 - 19:12 Masks 19:12 - 26:50 Multi-head attention 26:50 - 32:48 Positions, norms and feed-forward 32:48 - 38:25 The full Transformer 38:25 - 44:30 Training and inference 44:30 - 46:50 Summary Subscribe for more: https://www.youtube.com/@mehdihosseinimoghadam GitHub: https://github.com/mehdihosseinimoghadam LinkedIn: https://linkedin.com/in/mehdi-hosseini-moghadam-384912198 #Transformer #Attention #SelfAttention #MultiHeadAttention #PyTorch #DeepLearning #MachineLearning #LLM #NLP #AI #Python #AttentionIsAllYouNeed #ArtificialIntelligence